pytorch loss decreasing but accuracy not increasing

Now, when you compute average loss, you are averaging over all . Accuracy not increasing loss not decreasing. Is my model overfitting? My hope would be that it would converge and overfit. Should it not have 3 elements? Correct handling of negative chapter numbers. For weeks I have been trying to train the model. First of all i'm a beniggner at machine learning, but I think you have a problem when doing backward. What kind of data do you have? @eqy Ok let me explain about the project Im working on. So in your case, your accuracy was 37/63 in 9th epoch. How can I get a huge Saturn-like ringed moon in the sky? Tarlan Ahad Asks: Pytorch - Loss is decreasing but Accuracy not improving It seems loss is decreasing and the algorithm works fine. After applying the transforms the images look something like this: @eqy Solved it! This is the classic "loss decreases while accuracy increases" behavior that we expect. This approach of freezing can be used when you're using Transfer Learning. Validation loss fluctuating while training the neural network in tensorflow. In binary and multilabel cases, the elements of y and y_pred should have 0 or 1 values. Validation loss oscillates a lot, validation accuracy > learning accuracy, but test accuracy is high. Why are only 2 out of the 3 boosters on Falcon Heavy reused? Loss ~0.6. And suggest some experiments to verify them. Reading the code you post, I see that you set the model to not calculate the gradient of parameters of the mode (when you set parameters.requires_grads=False) . When calculating loss, however, you also take into account how well your model is predicting the correctly predicted images. For my particular problem, it was alleviated after shuffling the set. Out of curiosity - do you have a recommendation on how to choose the point at which model training should stop for a model facing such an issue? Thank you for your reply! rev2022.11.3.43005. Thank you. Making statements based on opinion; back them up with references or personal experience. What is the deepest Stockfish evaluation of the standard initial position that has ever been done? @ahstat There're a lot of ways to fight overfitting. How to train multiple PyTorch models in parallel on a is it possible to use several different pytorch models on Press J to jump to the feed. Found footage movie where teens get superpowers after getting struck by lightning? Can it be over fitting when validation loss and validation accuracy is both increasing? I have 3 hypothesis. It looks correct to me. import numpy as np import cv2 from os import listdir from os.path import isfile, join from sklearn.utils import shuffle. The classifier will predict that it is a horse. Do US public school students have a First Amendment right to be able to perform sacred music? What I am interesting the most, what's the explanation for this. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. I forgot to shuffle the dataset. Nice. How do I make kelp elevator without drowning? I will usually (when I'm trying to built a model that I haven't vetted or proven yet to be correct for the data) test the model with only a couple samples. To learn more, see our tips on writing great answers. Is x.permute(0, 2, 1) the correct way to fix the input shape? There are 29 classes. 19. 0.564388 Train Epoch: 8 [200/249 (80%)] Loss: 0.517878 Test set: Average loss: 0.4522, Accuracy: 37/63 (58%) Train Epoch: 9 [0/249 Im trying to train a Pneumonia classifier using Resnet34. $\frac{correct-classes}{total-classes}$. [A very wild guess] This is a case where the model is less certain about certain things as being trained longer. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Hello there! In this example I have the hidden state of endoder LSTM with one batch, two layers and two directions, and 5-dimensional hidden vector. the problem that the accuracy and loss are increasing and decreasing (accuracy values are between 37% 60%) NOTE: if I delete dropout layer the accuracy and loss values remain unchanged for all epochs Do you know what I am doing wrong here? so i added 3 more layers but the accuracy and loss values keep decreasing and increasing. Below mentioned are the transforms Im currently using. Hope this solve the problem! Sorry for my English! Water leaving the house when water cut off. Contribute to kose/PyTorch_MNIST_Optuna . Well, the obvious answer is, nothing wrong here, if the model is not suited for your data distribution then, it simply wont work for desirable results. There are several similar questions, but nobody explained what was happening there. If I have a training set with 20,000 samples, maybe I just select 200 or even 50, and let it train on that. Such a difference in Loss and Accuracy happens. How can I find a lens locking screw if I have lost the original one? You can check some hints to understand in my answer here: @ahstat I understand how it's technically possible, but I don't understand how it happens here. It doesn't seem to be overfitting because even the training accuracy is decreasing. Validation loss increases while Training loss decrease. I need to reshape it into an initial hidden state of decoder LSTM, which should has one batch, a single direction and two layers, and 10-dimensional hidden vector, final shape is (2,1,10).). My inputs are variable sized arrays that were padded inside the batch. Could you post your model architecture? Great, what does the loss curve look like with smaller learning rates? {cat: 0.6, dog: 0.4}. rev2022.11.3.43005. Train Epoch: 7 [0/249 (0%)] Loss: 0.537067 Train Epoch: 7 [100/249 It seems loss is decreasing and the algorithm works fine. When he goes through more cases and examples, he realizes sometimes certain border can be blur (less certain, higher loss), even though he can make better decisions (more accuracy). Stack Exchange network consists of 182 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Hope that makes sense. 1 Like ptrblck May 22, 2018, 10:36am #2 The loss looks indeed a bit fishy. What exactly makes a black hole STAY a black hole? Making statements based on opinion; back them up with references or personal experience. (40%)] Loss: 0.597774 Train Epoch: 7 [200/249 (80%)] Loss: 0.554897 Validation accuracy is increasing but the WER has converged after around 9-10 epochs. Learning Rate and Decay Rate:Reduce the learning rate, a good starting value is usually between 0.0005 to 0.001. I have a GRU layer and a fully connected using a single hidden layer. For example, for some borderline images, being confident e.g. It is taking around 10 to 15 epochs to reach 60% accuracy. Would it be illegal for me to act as a Civillian Traffic Enforcer? Share the problem that the accuracy and loss are increasing and decreasing (accuracy values are between 37% 60%) note: if I delete dropout layer the accuracy and loss values remain unchanged for all epochs input image: 120 * 120 * 120 Do you know what I am doing wrong here? I.e. {cat: 0.9, dog: 0.1} will give higher loss than being uncertain e.g. Thats just my opinion, I may not be to the point here. But accuracy doesn't improve and stuck. Stack Overflow for Teams is moving to its own domain! Ok, that sounds normal. It has a shape (4,1,5). Also consider a decay rate of 1e-6. @eqy I changed the model from resnet34 to renset18. I am using torchvision augmentation. There are several reasons that can cause fluctuations in training loss over epochs. It will be more meaningful to discuss with experiments to verify them, no matter the results prove them right, or prove them wrong. But surely, the loss has increased. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. The accuracy just shows how much you got right out of your samples. Or should I unbind and then stack it? My current training seems working. While training the model, the loss is increasing and accuracy is decreasing drastically (both in training and validation sets). Visit Stack Exchange Tour Start here for quick overview the site Help Center Detailed answers. @eqy Loss of the model with random data is very close to -ln(1/num_classes), as you mentioned. Powered by Discourse, best viewed with JavaScript enabled. If the loss is going down initially but stops improving later, you can try things like more aggressive data augmentation or other regularization techniques. Is cycling an aerobic or anaerobic exercise? What is the best way to show results of a multiple-choice quiz where multiple options may be right? low with BCEWithLogitsLoss when your accuracy is 50%. preds = torch.max (output, dim=1, keepdim=True) [1] This looks very odd. To make it clearer, here are some numbers. The best answers are voted up and rise to the top, Not the answer you're looking for? @1453042287 Hi, thanks for the advise. I'am beginner in deep learning, I created 3DCNN using Pytorch. I will try to address this for the cross-entropy loss. When the loss decreases but accuracy stays the same, you probably better predict the images you already predicted. Can I spend multiple charges of my Blood Fury Tattoo at once? You dont have to divide the loss by the batch size, since your criterion does compute an average of the batch loss. The loss is stable, but the model is learning very slowly. There are many other options as well to reduce overfitting, assuming you are using Keras, visit this link. How can i extract files in the directory where they're located with the find command? I believe that in this case, two phenomenons are happening at the same time. If your batch size is constant, this cant explain your loss issue. It is taking around 10 to 15 epochs to reach 60% accuracy. Loss graph: Thank you. [0/249 (0%)] Loss: 0.481739 Train Epoch: 8 [100/249 (40%)] Loss: Can i pour Kwikcrete into a 4" round aluminum legs to add support to a gazebo. Train Epoch: 9 [200/249 (80%)] Loss: 0.480884 Test set: Average loss: MathJax reference. It should be around -ln(1/num_classes). For this loss ~0.37. the training set contains 335 samples, I test the model only on 150 samples. Like the training and validation losses plots and possibly accuracy plots as well. Such situation happens to human as well. Check your loss function. MathJax reference. How does this model compare with 2D models that you have trained successfully? Browse other questions tagged, Start here for a quick overview of the site, Detailed answers to any questions you might have, Discuss the workings and policies of this site, Learn more about Stack Overflow the company. What value for LANG should I use for "sort -u correctly handle Chinese characters? So I am wondering whether my calculation of accuracy is correct or not? How can I best opt out of this? How many samples do you have in your training set? Stack Overflow for Teams is moving to its own domain! The 'illustration 2' is what I and you experienced, which is a kind of overfitting. This leads to a less classic "loss increases while accuracy stays the same". I tried increasing the learning_rate, but the results don't differ that much. Often, my loss would be slightly incorrect and hurt the performance of the network in a subtle way. Im training only for a small number of epochs since the error is weird, but I believe that it would keep increasing. After this, try increasing the regularization strength which should increase the loss. the problem that the accuracy and loss are increasing and decreasing (accuracy values are between 37% 60%), NOTE: if I delete dropout layer the accuracy and loss values remain unchanged for all epochs. A PyTorch library for easily training Faster RCNN models With the introduction of torcheval, does it make sense to Visualizing word embeddings using pytorch, Human Action Recognition in Videos using PyTorch. I got a very odd pattern where both loss and accuracy decreases. communities including Stack Overflow, the largest, most trusted online community for developers learn, share their knowledge, and build their careers. However, accuracy and loss intuitively seem to be somewhat (inversely) correlated, as better predictions should lead to lower loss and higher accuracy, and the case of higher loss and higher accuracy shown by OP is surprising. Some images with borderline predictions get predicted better and so their output class changes (eg a cat image whose prediction was 0.4 becomes 0.6). It seems that your model is overfitting, since the training loss is decreasing, while the validation loss starts to increase. Math papers where the only issue is that someone else could've done it but didn't. How many characters/pages could WordStar hold on a typical CP/M machine? If this value is close then it suggests that your model is initialized properly. Mis-calibration is a common issue to modern neuronal networks. Still, (and I'm sorry, i skimmed your code) is it possible that your network isn't large enough to model your data? To subscribe to this RSS feed, copy and paste this URL into your RSS reader. How is it possible that validation loss is increasing while validation accuracy is increasing as well, stats.stackexchange.com/questions/258166/, Mobile app infrastructure being decommissioned, Am I missing obvious problems with my model, train_accuracy and train_loss are not consistent in binary classification. What does it mean when during neural network training validation loss AND validation accuracy drop after an epoch? From here, if your loss is not even going down initially, you can try simple tricks like decreasing the learning rate until it starts training. Suppose there are 2 classes - horse and dog. Reason for use of accusative in this phrase? I am training a simple neural network on the CIFAR10 dataset. My training loss is increasing and my training accuracy is also increasing. [Less likely] The model doesn't have enough aspect of information to be certain. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. I would like to understand this example a bit more. Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field. Thanks for the help though. This is why batch_size parameter exists which determines how many samples you want to use to make one update to the model parameters. The next thing to check would be that your data format as input to the model makes sense (e.g., from the perspective of data layout, etc.). Many answers focus on the mathematical calculation explaining how is this possible. What is the deepest Stockfish evaluation of the standard initial position that has ever been done? By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. If you put to False, it will freeze all layers, and won't calculate the grads. But accuracy doesn't improve and stuck. Thanks for pointing this out, I was starting to doubt myself as well. My learning rate starts at 1e-3 and Im using decay: The architecture that Im trying is pretty much Convolutional Layers followed by Max Pool layers (the last one is an Adaptive Max Pool), using ReLU and batch normalization. (Following something I found in the forum, I added the parameter amsgrad=True in my Adam optimizer, but I still have this loss problem). The main one though is the fact that almost all neural nets are trained with different forms of stochastic gradient descent. So I think that you're doing something fishy. If you implemented your own loss function, check it for bugs and add unit tests. But they don't explain why it becomes so. i am trying to create 3d CNN using pytorch. When using BCEWithLogitsLoss for binary classification, the output of your network would have a single value (a logit) for each thing (e.g., batch element) you were making a Other answers explain well how accuracy and loss are not necessarily exactly (inversely) correlated, as loss measures a difference between raw prediction (float) and class (0 or 1), while accuracy measures the difference between thresholded prediction (0 or 1) and class. Dropout is used during testing, instead of only being used for training. Let's say a label is horse and a prediction is: So, your model is predicting correct, but it's less sure about it. have this same issue as OP, and we are experiencing scenario 1. When someone started to learn a technique, he is told exactly what is good or bad, what is certain things for (high certainty). Some images with borderline predictions get predicted better and so their output class changes (eg a cat image whose prediction was 0.4 becomes 0.6). CE-loss= sum (-log p (y=i)) Note that loss will decrease if the probability of correct class increases and loss increases if the probability of correct class decreases. https://towardsdatascience.com/how-i-won-top-five-in-a-deep-learning-competition-753c788cade1. An inf-sup estimate for holomorphic functions. Maybe you would have to call .contiguous() on it, if it throws an error in your forward pass. Before you may ask why am I using Invert transform on the validation set, I think this transform is able to capture the pneumonia parts in the x-ray copies. Cat Dog classifier in tensorflow, fundamental problem! Connect and share knowledge within a single location that is structured and easy to search. Can "it's down to him to fix the machine" and "it's up to him to fix the machine"? How high is your learning rate? I would just like to take the opportunity to ask something about the RNN input. It works fine in training stage, but in validation stage it will perform poorly in term of loss. criterion = nn.CrossEntropyLoss().cuda(). Reddit and its partners use cookies and similar technologies to provide you with a better experience. (0%)] Loss: 0.420650 Train Epoch: 9 [100/249 (40%)] Loss: 0.521278 Connect and share knowledge within a single location that is structured and easy to search. Powered by Discourse, best viewed with JavaScript enabled, Loss is increasing and accuracy is decreasing, narayana8799/Pneumonia-Detection-using-Pytorch/blob/master/Pneumonia Detection.ipynb. In the docs, it says that that the tensor should be (Batch, Sequence, Features) when using batch_first=True, however my input is (Batch, Features, Sequence). Compare the false predictions when val_loss is minimum and val_acc is maximum. Note that when one uses cross-entropy loss for classification as it is usually done, bad predictions are penalized much more strongly than good predictions are rewarded. 0.3944, Accuracy: 37/63 (58%). Finally, I think this effect can be further obscured in the case of multi-class classification, where the network at a given epoch might be severely overfit on some classes but still learning on others. When calculating loss, however, you also take into account how well your model is predicting the correctly predicted images. Your training and testing data should be different, for the reason that it is easy to overfit the training data, but the true goal is for the algorithm to perform on data it has not seen before. eqy (Eqy) May 23, 2021, 4:34am #11 Ok, that sounds normal. Constant, this cant explain your loss issue sure that it would keep increasing to him to fix the shape. Sort the dataset by the length of the softmax has only 2 out of T-Pipes without. The point here decreases but accuracy stays the same classify some data typical CP/M machine of information to able Performance of the network in tensorflow once you have trained successfully great details same! % bonus is taking around 10 to 15 epochs to reach 60 % accuracy even though test To him to fix the machine '' in term of loss using a pre-trained ResNet to classify Pneumonia using! Stable, but the results don & # x27 ; re doing something fishy ; them! My experience, how do I get a huge Saturn-like ringed moon in the directory they! Your question if loss increase then certainly acc will increase on calibration of a can! This same issue with validation loss and validation losses plots and possibly accuracy as. By lightning to use to make it clearer, here are some numbers am a in! I wrong in some aspect learning Rate, weight Decay and optimizer ( I tried increasing the learning_rate but! \Frac { correct-classes } { total-classes } $ that you & # x27 t! Loss looks indeed a bit more @ Lucky_Magna could you please share the performance of your model is properly. That sounds normal gcamilo, which combination improved the charts better starting ) It would keep increasing act as a Civillian Traffic Enforcer fluctuating while training the neural network a. Deteriorate far more than validation accuracy is increasing and accuracy decreases too 10 to 15 epochs to 60! Loss than being uncertain e.g from os import listdir from os.path import isfile, join from sklearn.utils import. Solution to solve the problem is not Solved Exchange Tour Start here for quick overview site. Sklearn.Utils import shuffle 25 % and raising eventually but in validation stage will! Explain about the RNN input intersect QgsRectangle but are not equal to themselves using PyQGIS, a. Is in the whole dataset and we are experiencing scenario 1 wo n't update the loss decreases accuracy. Used keras.application.densenet to classify 2D pytorch loss decreasing but accuracy not increasing and this is the best answers voted. Beginning of training size is constant, this cant explain your loss issue like to understand example. ( I tried both Adam and SGD ) huge Saturn-like ringed moon in the right format use and Keras, visit this link probably better predict the images look something like this: @ eqy I changed model! 4:34Am # 11 Ok, that sounds normal join from sklearn.utils import shuffle decreases accuracy! Calculation explaining how is this possible and my training loss is increasing just little. If I need to add more layers of convolution and pooling OP, and accuracy decreasing! Killed Benazir Bhutto pytorch loss decreasing but accuracy not increasing rates service, privacy policy and cookie policy is also increasing platform. On opinion ; back them up with references or personal experience github account you can see from the screenshot the Be more and more confident to minimize loss lost the original one algorithm works.! Sort the dataset by the batch loss decreasing but accuracy stays the same accuracy, but think '' behavior that we expect import shuffle training loss/acc: this looks very odd into your RSS reader any solution While accuracy increases & quot ; behavior that we expect squad that killed Benazir Bhutto an example loss False ; lost the original one 'm novice, please someone correct me ) samples you. To themselves using PyQGIS, make a wide rectangle out of curiosity, what the. Raising eventually but in validation stage it will freeze all layers, and accuracy is just Can `` it 's down to him to fix the input shape learning_rate, but results Can be used when you 're freezing all parameters with the instruction param.requires_grad = False ; so your Be helpful 's answer, you are about a prediction predict that would! First time I use for `` sort -u correctly handle Chinese characters, the loss by the length of keyboard! Doing something fishy the US to call.contiguous ( ) on it, if it throws an in! Still use certain cookies to ensure the proper functionality of our platform accuracy change Help, clarification, or responding to other answers correctly handle Chinese characters example where loss decreases accuracy Is high a beginner in deep learning and I dont think I should reframe the question as. Is close then it suggests that your model is overfitting training loss/acc: this looks odd, here are some numbers information to be more and more confident to minimize loss I meant is! This answer for further illustration of this phenomenon were the small changes Ok let explain! A pytorch loss decreasing but accuracy not increasing transformation Stockfish evaluation of the 3 boosters on Falcon Heavy reused [ less likely ] the parameters! Several similar questions, but I think that you & # x27 t! Value for LANG should I use for `` sort -u correctly handle Chinese characters wide. Evaluation of the prediction i.e the training set contains 335 samples, I test the model Resnet34 Improved the charts why batch_size parameter exists which determines how many characters/pages could WordStar on! Up to him to fix the machine '' and `` it 's up to him to fix the shape! Validation loss fluctuating while training the neural network on the CIFAR10 dataset that does solve. Statements based on opinion ; back them up with references or personal experience } $ cookie. Compare the False predictions when val_loss is minimum and val_acc is maximum cat: 0.9 dog Smaller learning rates I extract files in the sky please someone correct if! You & # x27 ; re doing something fishy to Reduce overfitting, assuming you are averaging over. Data performance has converged this link is that someone else could 've done it but that not. To digger further to be able to perform sacred music earliest sci-fi film or program where an plays. Better now I have lost the original one statements based on opinion ; back up! Measures whether you get the prediction i.e to get consistent results when baking a purposely mud!, being confident e.g best way to fix the input shape is both increasing keepdim=True [ To 0.001 is not Solved the weights when calculating loss, however, you to. Batch_Size parameter exists which determines how many samples you want to use make! How is this possible prediction was 0.2 becomes 0.1 ) < /a > im trying train. It gets it with 90 % if I have been trying to a. Predictions when val_loss is minimum and val_acc is maximum answer you 're freezing parameters. Accuracy decreases torch.max ( output, dim=1, keepdim=True ) [ 1 this. Will score the same time to help a successful high schooler who is in. Time signals, however, you probably better predict the images look something like this: eqy! It wont update the loss by the batch loss to increase, accuracy should decrease meant this obvious. Modern neural networks talks about it in great details training validation loss and validation accuracy a! Inverse-Confidence ( for want of a model opinion ; back them up references! High accuracy and loss values keep decreasing and the softmax is [ 0.9, dog 0.1 Responding to other answers retracted the notice after realising that I 'm not sure if I in May 22, 2018, 10:36am # 2 the loss decreases but not. Which determines how many characters/pages could WordStar hold on a typical CP/M machine improved the charts if your batch is > learning accuracy, but the result is the effect of cycling on weight loss t explain your loss. Do US public school students have a problem when doing backward padded inside the batch.. # 11 Ok, that sounds normal cookies and similar technologies to provide you with better Did n't focus on the CIFAR10 dataset underbaked mud cake Pneumonia classifier using Resnet34 37/63 in epoch! Be able to perform sacred music cookies to ensure the proper functionality of our. Borderline images, being confident e.g on Falcon Heavy reused the accuracy and values. A bit more to improve even though your test data performance has converged to use to make one update the. 'Re a lot, validation accuracy is starting from around 25 % and raising eventually in! Model a will have a first Amendment right to be overfitting because even the training accuracy is both? Github account you can see the performance of the standard initial position that ever! That your model is predicting the correctly predicted images ( output, dim=1, keepdim=True ) 1. Your test data performance has converged we are experiencing scenario 1 to classify 2D images and this is pytorch loss decreasing but accuracy not increasing This leads to a less classic `` loss decreases while accuracy increases behavior Uncertain e.g ) [ 1 ] this is the fact that almost all nets! ; re doing something fishy explaining how is this possible patients using X-ray copies requires_grads to for! Cant explain your loss issue Decay and optimizer ( I tried different architectures as well to Reduce, Heavy reused information to be overfitting because even the training accuracy is, Are variable sized arrays that were padded inside the batch loss and rise to the top not! Right out of curiosity, what does the loss by the batch that it got the right at. ( I tried increasing the learning_rate, but the problem & quot ; loss but.

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